PRICE ESTIMATION OF SELECTED GRAINS PRODUCTS BASED ON MACHINE LEARNING FOR AGRICULTURAL ECONOMIC DEVELOPMENT IN TÜRKİYE

KESKİN A., ERSİN İ., ATALAN A.

Journal of Animal and Plant Sciences, vol.34, no.5, pp.1290-1302, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 5
  • Publication Date: 2024
  • Doi Number: 10.36899/japs.2024.5.0811
  • Journal Name: Journal of Animal and Plant Sciences
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Business Source Elite, Business Source Premier, CAB Abstracts, Veterinary Science Database
  • Page Numbers: pp.1290-1302
  • Keywords: Agricultural products, barley, bread wheat, corn, durum wheat, grains, machine learning algorithms, price estimation
  • Istanbul Medipol University Affiliated: Yes

Abstract

This study aims to estimate the price fluctuations of essential grain products, namely bread wheat (Triticum aestivum), durum wheat (Triticum durum), barley (Hordeum vulgare), and corn (Zea mays), in Türkiye using machine learning (ML) algorithms. Using data from January 2, 2020, to January 10, 2023, the study employs algorithms such as random forest (RF), neural network (NN), support vector machine (SVM), and linear regression (LR). Independent variables include oil prices, currency exchange rates, and grain production volumes. The random forest (RF) algorithm provided the best results with the highest R² values, while NN and LR showed relatively lower performance. The study highlights the significant impact of production and consumption volumes on grain prices and underscores the importance of ML algorithms in predicting these prices amidst changing conditions. Investments in agricultural technologies should be increased to improve data collection and analysis processes, as this is crucial for preventing price fluctuations in the agricultural sector.